Professor Andrew Ng:

I’m excited to share with you the AI Transformation Playbook: https://lnkd.in/gJMf_Pq

Drawn from my experience leading Google Brain, Baidu’s AI Group, and Landing AI, this Playbook provides a roadmap for your company to transform into a great AI company.

You can download a free copy of the AI Transformation Playbook here: https://lnkd.in/gJMf_Pq

Building strong AI capabilities for your company will require a long-term investment, but it is feasible for most enterprises. The AI Transformation Playbook will walk you through the following five steps:

1. Execute pilot projects to gain momentum
2. Build an in-house AI team
3. Provide broad AI training
4. Develop an AI strategy
5. Develop internal and external communications

Many of the biggest untapped opportunities in AI lie outside the software industry. I hope that this AI Transformation Playbook will help your company become an AI leader in your industry vertical.

Download your free copy of the AI Transformation Playbook here: https://lnkd.in/gJMf_Pq


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Professor Yann LeCun
‏
At NYU, even the art school has a course on deep learning, complete with ConvNets, GANs and RL for artistic creation.

The class is called "neural aesthetics" and is taught by Gene Kogan in the ITP Master's program at the NYU Tisch..

http://ml4a.github.io/classes/itp-F18/

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Understanding Supervised, Unsupervised, and #ReinforcementLearning (RL) — getting under the hood with RL: http://bit.ly/2BNrJ1u #abdsc #BigData #DataScience #MachineLearning #Algorithms #AI



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PyText: Industrial-strength open source NLP package from Facebook AI: develop NLP models on PyTorch and deploy through ONNX.

Pre-trained models for text classification, sequence tagging, joint intent-slot...

https://t.co/phn8mu9Dhz

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Best paper award #NeurIPS2018 main idea: Defining a deep residual network as a continuously evolving system & instead of updating the hidden units layer by layer, define their derivative with respect to depth instead. Paper: https://arxiv.org/pdf/1806.07366.pdf … Code: https://github.com/rtqichen/torchdiffeq

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✨📒 Did you know that you can connect to GoogleColab using a local runtime, or a virtual machine running in the cloud (AWSCloud, GoogleCloud, Azure, etc.)? 👉 Check out our guide + blogpost for how to set up your environment: https://research.google.com/colaboratory/local-runtimes.html … https://blog.kovalevskyi.com/gce-deeplearning-images-as-a-backend-for-google-colaboratory-bc4903d24947


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Stanford Tracking Artificial Intelligence Research To See Future - Palo Alto, CA Patch

Read more here: https://ift.tt/2PCc1JY

#ArtificialIntelligence #AI #DataScience #MachineLearning #BigData #DeepLearning #NLP #Robots #IoT


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linux
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What's the hardest part of ML? The most expensive? The most time-consuming? Choosing from:
- data collection & labelling
- data cleaning
- modelling / science
- implementation
- infrastructure / cloud SysOps
- deployment
- maintenance

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Attention Networks with Keras The "Attention Network" is one of the most interesting advancements in natural language processing. So, what makes an attention network tick & why it's special?

https://buff.ly/2LNaK0K

#NLP #NeuralNetworks #AI

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hardest: features and parameters of the model, most expensive: data collection, cleaning and labeling, most time consuming: multiple iterations in order to converge to the optimal parameters, testing & evaluation.

Dr François Chollet

This is a great answer and I agree -- modelling/science is the hardest (if you want to do it right), and also the most time-consuming due to lengthy iterations. Meanwhile data collection and labelling is the most expensive, and often the most important to the success of a project.

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"A Brief Introduction to Machine Learning for Engineers"

By Osvaldo Simeone: https://lnkd.in/eT9FVYd

#ArtificialIntelligence #MachineLearning #NeuralNetworks


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A Full Hardware Guide to Deep Learning

By Tim Dettmers: https://lnkd.in/emiGW6p

#ai #deeplearning #gpu #gpus #hardware


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Playing first-person shooter games with webcam and #DeepLearning (Tensorflow #ObjectDetection)

Find out how you can use an object detection model to control and play any first-person shooter game with your computer's webcam. Links to the code below.

Full Video: https://lnkd.in/eBq7z4r

Blog: https://lnkd.in/eekrqWk

Code: https://lnkd.in/ekhwwiJ

Subscribe: youtube.com/c/DeepGamingAI

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Want to learn ML through code examples?

Check out these 5 scikit-learn tutorials to get started:

1. Randomized search vs grid search - https://lnkd.in/gjHpjJK

2. Using regularization to improve your GBM models - https://lnkd.in/gYNCNGD

3. Selecting the correct number of estimators for GBM models - https://lnkd.in/gW5AQTk

4. Selecting the correct number of estimators for random forest models - https://lnkd.in/ge66wUH

5. Decision boundary comparison for popular classifier models (check out this viz!) - https://lnkd.in/gHVg9nm

There are a ton more that you can go through on the sk-learn tutorial page as well.

👉 Check them out here - https://lnkd.in/gAv3hq7

👉 If you need more help learning machine learning or getting a job as a data scientist, then hop on my email list and I'd be happy to help - https://lnkd.in/g7AYg72

#datascience #machinelearning

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A simple notebook to remove the background of objects using Mask R-CNN

By Zaid Alyafeai: https://lnkd.in/exr7yWi

#artificialinteligence #deeplearning #machinelearning #tensorflow

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Ten Simple Rules for Reproducible Research in Jupyter Notebooks

Rule et al.: https://lnkd.in/efWmkyi

#BigData #ComputerScience #DataScience #MachineLearning

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Tuning Machine Learning Hyperparameters

https://heartbeat.fritz.ai/tuning-machine-learning-hyperparameters-40265a35c9b8

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if you're going to start a #ML services startup, make it about data collection and labelling. This is main pain point, and where the most value can be unlocked.

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🗣 @AI_Python_arXiv
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